Mall Walking Program For Stroke Survivors : Can It Help Combat Post-Stroke Social Isolation?
Bibliographic record
Abstract
D. Schreiber1, J. Jukes2, D. Pal1, D. Mackay2.1March of Dimes Canada, Research and Quality, Toronto, Canada.2March of Dimes Canada, Community Engagement and Integration Services, Toronto, Canada.Abstract TextWhile social isolation and loneliness prior to a stroke can lead to poorer outcomes, the impact of social isolation after a stroke can be just as devastating. As stroke can affect physical and cognitive function, eating, swallowing, language, and speech, it can be difficult for stroke survivors to interact with friends/families and to take part in activities they once enjoyed.Next Steps is a weekly mall walking program specifically geared towards stroke survivors. It evolved from a stroke rehabilitation walking program delivered by rehabilitation therapists as a short-term program to reintroduce people to physical activity in the community post-stroke. The rehabilitation therapists identified a need for an ongoing community program to transition the stroke survivors into and partnered with non-profit organizations to deliver Next Steps. The majority of participants are referred to Next Steps from a stroke recovery program or rehabilitation centre.Participants in the program were surveyed to evaluate both their improvement since starting the program as well as their satisfaction with the program. While the most common reasons cited for joining Next Steps was to increase physical activity, stamina, and strength, the greatest improvement participants identified was increased social activity. Results from a thematic analysis of the survey responses prominently outline the significance of the program's social aspects. Increased social activity, community engagement, independence, and confidence will be explored in the presentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".